A multi-agent-based model for sustainable governance of urban flood risk mitigation measures

• Published in: Natural hazards (Dordrecht) 2020-10, Vol.104 (1), p.1079-1110
• Main Authors: Koc, Kerim, Işık, Zeynep
• Format: Article
• Language: English
• Subjects: Agent-based models; Civil Engineering; Decision making; Dimensions; Earth and Environmental Science; Earth Sciences; Economic models; Environmental effects; Environmental Management; Environmental risk; Flood damage; Flood risk; Flooding; Floods; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Governance; Hydrogeology; Mitigation; Model testing; Multiagent systems; Natural Hazards; Original Paper; Risk; Risk reduction; Sensitivity analysis; Sustainability; Three dimensional models; Urbanization
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-020-04205-3

Consequences of urban floods increased and diversified in terms of social, economical and environmental effects, due to the dense and unplanned urbanization in areas at risk of flooding. Reducing the potential damage of flood is one of the most effective and sustainable reduction strategies of flood risks through adopting adequate urban flood risk mitigation measures (UFRM). The main objective of this study is to ascertain urban floods and develop a model identifying UFRM in accordance with their importance in reducing the effects of floods by employing the concept of multi-agent systems. Social, economical and environmental-based agents were modelled in the agent environment to consider the three dimensions of sustainability in the proposed model. Thirteen negotiation strategies were developed for the agents to negotiate with each other. An illustrative case study was then performed to test the proposed model features, and the results were thoroughly analysed. Sensitivity analysis was conducted to specify the most sensitive strategies over the changing fuzziness level of agents. It was observed that the best strategy differs with respect to short-, medium- and long-term gains significantly, which, in turn, lead to alterations in the list of UFRM. This could provide different risk mitigation plans for different regions with their changing resources. In summary, this study provides an innovative multi-agent-based model that can be performed to specify the most urgent UFRM which can be used by disaster coordination and management authorities as a decision-making input.